From Syntax to Semantics: AI Assisted Computational Linguistics in the Era of Large Computational Language Models

The large language computational methods have brought about significant changes in the area of linguistics. The previous systems statically followed the rules of grammar to comprehend the connotation of definition and context in words. The proposed method focuses a novel method that intentions to enhance the depth, accuracy and sense of words through understanding of natural language. The available methods follow patterns of words in sentences and patterns. Although, it is helpful, the traditional methods struggle to find the meanings deeply, such as structure, language style, and shifts of word context. To overcome these challenges, the proposed method joins tasks-based definition alterations and pre-trained largescale language methods. The novel method follows two phases: initial phase placed efforts to organize the structure of sentence, describing words and in the following phase it focused to present definition fine-tunes such as improving sense of word, inference of the word context, and sentiment explanation. The method combines contrastive learning to find relationships of subtle definitions in large and multifaceted sentences. Using datasets, experimentations were directed. The datasets of GLUE and SamEval were used along with similarity of sentence, question and answering based on context and entailment of text. The outcomes of proposed method are F1-score, accuracy and scores of BLEU as well. Results showed a 6-13% than traditional works specifically in handling of reasoning of multi-sentence, clarification of context. The enhanced method works perfectly across tasks and languages suits in platforms of multilingual and several language applications, language assistants and customer care service centres.